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Till a few years ago, help features that were accessed by using the F1 key on the keyboard were the life support of tedious and complicated software applications. As businesses move into the fast lane with time being a major constraint, the clichéd click-based directions provided by help files have become passé. Today, voice-based searches and voice-activated assistance (for example: Alexa) have become the new normal. These appendages are powered by technologies such as Artificial Intelligence (AI) / Machine Learning (ML).
Similarly, as data proliferation increases, it becomes highly important to seek out smarter ways of data analysis for continued progress to keep pace with the fast movers in the industry. In recent years, ML has emerged as the vital solution to search and analyze humongous amounts of data for deriving quick, actionable insights. Artificial Neural Networks (ANNs) have also proved to be a breakthrough in this area.
ML algorithms enable machines to become intelligent. It helps machines to recognize patterns, construct prediction models, or classify images or videos through self-learning to aid faster search and retrieval. ML algorithms can be implemented using a wide variety of methods such as decision trees, random forest, clustering, neural network, and more. Artificial Neural Networks (ANNs) come under Deep Learning, which in nothing but ML, and ML is a subfield of AI.
To put it simply, ANNs are mathematical models that are inspired by the way in which biological neural networks in the human brain process the information. ANNs are capable of self-learning as well as pattern recognition. They are generally presented as systems of highly interconnected "neurons", which can compute values from inputs.
The input and output of the ANN can be compared to dendrites and axons while weight multiplication can be compared to the synapses of a biological neural network.
In real-world business problems, we usually have some collected data, i.e. a knowledge base, upon which we need to train algorithms and create a model for predictions, classification, or any other processing. Based on behavior during the training and the nature of knowledge base, the learning is divided into three classes:
Supervised Learning is the most commonly used form of ANN. Here, we have a knowledge base that contains a vector of input values and a vector of desired output values. Once the network calculates the output for one of the inputs, the cost function (for example: mean squared error function) calculates the error vector. This error indicates how close the guess is to the desired output. Now, this error is sent back to the neural network, and weights are modified accordingly. This process is called Backpropagation. It is an advanced mathematical algorithm, using which the ANN adjusts all weights at once.
The entire point of training is to set the correct values to the weights, in order to get the desired output in the neural network. This is required to make the value of the error vector as small as possible, i.e. to find a global minimum of the cost function. For this purpose, we use technique called gradient descent.
Some common types of neural networks include Recurrent Networks, Long Short Term Memory, Hopfield Networks, Restricted Boltzmann Machine, Convolutional Neural Networks, Sequence-to-Sequence Models, and Generative Adversarial Networks (GANs).
Together, these are used in real-time business applications as given below:
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An efficient ANN architecture can be used to solve complex non-linear business problems as mentioned above instead of defining functions with high degree polynomials, which may lead to over fitting. Parallel processing of humongous data using ANNs expedites analysis and saves time, effort, and money as compared to other conventional approaches. This is quite the principle used in quickly responding to voice searches.